The present invention relates to a method of compressing and decompressing a multi-dimensional image. Particularly, the present invention relates to a method of effectively compressing and decompressing a multi-dimensional image for image transmission between apparatuses, such as, those having different types of data transmitters and receivers, in which pixels or frames on the time axis do not necessarily correspond to each other on the compression side and the decompression side.
Several methods of compressing image data have been proposed. For example, there is a linear (equal) contouring method in which the sampled values of a digitized video signal are equally divided into signal levels and the sampled values included in each signal level are replaced with a representative value. According to this method, an amount of data per natural image in the range of 6 bits (64 gradations) to 8 bits (256 gradations) is generally necessary in order to render negligible the differences between the representative values and original values. A lot of data thus should be processed per sampled value when a video signal digitized by equal quantization is merely recorded.
In a conventional method, digital data compressed by an effective coding method is recorded or transmitted and then reproduced or received, and decompressed to reproduce an image. In this method, human characteristics are exploited, such as vision or hearing, both of which are sensitive to only small changes not big changes in a signal portion. Alternatively, correlation is utilized between signals to be recorded on the time-spacial axis. Under such utilization, an image is divided into pixels and a few approximated values of the original image are transmitted using the level of correlation between luminance values of pixels close to each other. Alternatively, differences between pixels or frames are transmitted. An amount of data per sampled value may be decreased by decreasing frequency components using the fact that high frequency components are less prevalent in an image than low frequency components.
The conventional method mostly requires the number of pixels to be equal between the original and reproduced (decompressed) images because reproduction of divided pixels must be well reproduced. It is thus necessary that some pixels be interpolated or extracted after decompression when compression and decompression are conducted to images with different numbers of pixels. Consequently, this method depends, to some extent, on physical image components during reproduction, such as, the number of pixels, their shapes and the level of luminance, without selecting true data and reproducing the physical image components.
There is also the case wherein pixel density is extremely different in two images having different numbers of pixels. For example, an image taken by photographic equipment is used as a printing plate for an electric printer. The pixel density of the photographed image is at most 500.times.500 per image frame. Compared to this, the pixel density of the image for the electric printer is several thousand times that amount, which is extremely great. Aliasing occurs in this case even though the conventional method for the same number of pixels between two images is not employed.
Further, the case where interpolation is conducted without increasing the number of pixels, since a large interpolation area is filled with average values of weighted data, image deterioration due to interpolation distortion cannot be avoided.
If the pixel density of an original image is multiplied several thousand times, since correlation between images close to each other is extremely high, principally effective image compression is possible. However, the conventional method which requires that the original and reproduced (decompressed) images have the same number of pixels, cannot achieve a high compression ratio.
The applicant of the present invention has already proposed the following multi-dimensional image compressing and decompressing method which solves such problems.
Firstly, feature points (pixels) of an image to be processed are selected to obtain image data in which the image is compressed while disregarding the level of pixel density. When decompressing, the original image is reproduced not using the image data but using feature points, such as, positive and negative maximum curvature points of the luminance contour lines on the luminance function of the original image with luminance data that two-dimensionally spreads out or of luminance contour planes on the luminance function of the original image with luminance data that three-dimensionally (including the time-axis) spreads out, as if a new image is displayed on another pixel density plane.
Alternatively, pixels are selected as feature points when a difference between the approximate line (plane) of a luminance contour line (plane) on the luminance function and the luminance contour line (plane) on each pixel is greater than a predetermined reference value. The data of positions and luminance values of the feature points thus obtained are transmitted or recorded.
When the data of positions and luminance values is used in image reproduction, luminance data on pixels not corresponding to the feature points is decided in decompression using an interpolation plane or solid formed by means of feature points close to the pixels which are not the feature points.
Alternatively, a plurality of luminance data is selected from the luminance function of image data that three-dimensionally spreads out over axes with the time-axis. Pixels are selected as feature points when a difference between the approximate line of a luminance contour line on the luminance function for the selected luminance data and the luminance contour line is greater than a predetermined reference value. The data representative of positions and luminance values of the feature points thus obtained is transmitted or recorded.
When the data representative of positions and luminance values is used in image reproduction, luminance data on pixels which are not the feature points is decided in decompression using an interpolation solid formed by means of feature points close to the pixel which are not the feature points.
In this method, however, texture data on an object surface in an image is mostly neglected during data compression, because the texture exists in an extremely small part of the image even though it is a very important characteristic of the object.
The conventional method mostly requires that original and reproduced (decompressed) images have the same number of pixels because the divided pixels must be well reproduced. Further in the case of motion picture, this conventional method requires the same number of images or frames per second. However, when image reproduction is made by compressing the luminance function composed of pixels, the texture data which is a small part of an object surface is mostly neglected and an original image cannot be well reproduced.
In order to provide further improvement in compression ratio without requiring the number of pixels or frames on the time-axis to be the same before compression and after decompression, the luminance function is expressed with fewer parameters. When decompressing in this case, the luminance function is reproduced generally using simple interpolation lines, planes or solids. The texture of an object surface is barely reproduced in this case and decompressed image quality is very much deteriorated.